Glycyrrhetinic acid 3--mono-β-d-glucuronide (GAMG), which possesses a higher sweetness and stronger pharmacological activity than those of glycyrrhizin (GL), can be obtained by removal of the distal glucuronic acid (GlcA) from GL. In this study, we isolated a -glucuronidase (TpGUS79A) from the filamentous fungus Li-93 that can specifically and precisely convert GL to GAMG without the formation of the by-product glycyrrhetinic acid (GA) from the further hydrolysis of GAMG. First, TpGUS79A was purified and identified through matrix-assisted laser desorption ionization-tandem time of flight mass spectrometry (MALDI-TOF-TOF MS) and deglycosylation, indicating that TpGUS79A is a highly -glycosylated monomeric protein with a molecular mass of around 85 kDa, including around 25 kDa of glycan moiety. The gene for TpGUS79A was then cloned and verified by heterologous expression in TpGUS79A belonged to glycoside hydrolase family 79 (GH79) but shared low amino acid sequence identity (<35%) with the available GH79 GUS enzymes. TpGUS79A had strict specificity toward the glycan moiety but poor specificity toward the aglycone moiety. Interestingly, TpGUS79A recognized and hydrolyzed the distal glucuronic bond of GL but could not cleave the glucuronic bond in GAMG. TpGUS79A showed a much higher catalytic efficiency on GL (/ of 11.14 mM s) than on the artificial substrate NP-glucopyranosiduronic acid (/ of 0.01 mM s), which is different from the case for most GUSs. Homology modeling, substrate docking, and sequence alignment were employed to identify the key residues for substrate recognition. Finally, a fed-batch fermentation in a 150-liter fermentor was established to prepare GAMG through GL hydrolysis by Li-93. Therefore, TpGUS79A is potentially a powerful biocatalyst for environmentally friendly and cost-effective production of GAMG. Compared to chemical methods, the biotransformation of glycyrrhizin (GL) into glycyrrhetinic acid 3--mono--d-glucuronide (GAMG), which has a higher sweetness and stronger pharmacological activity than those of GL, via catalysis by -glucuronidase is an environmentally friendly approach due to the mild reaction conditions and the high yield of GAMG. However, currently available GUSs show low substrate specificity toward GL and further hydrolyze GAMG to glycyrrhetinic acid (GA) as a by-product, increasing the difficulty of subsequent separation and purification. In the present study, we succeeded in isolating a novel-glucuronidase (named TpGUS79A) from Li-93 that specifically hydrolyzes GL to GAMG without the formation of GA. TpGUS79A also shows higher activity on GL than those of the previously characterized GUSs. Moreover, the gene for TpGUS79A was cloned and its function verified by heterologous expression in Therefore, TpGUS79A can serve as a powerful biocatalyst for the cost-effective production of GAMG through GL transformation.
Semantic segmentation is a key technology for remote sensing image analysis widely used in land cover classification, natural disaster monitoring, and other fields. Unlike traditional image segmentation, there are various targets in remote sensing images, with a large feature difference between the targets. As a result, segmentation is more difficult, and the existing models retain low accuracy and inaccurate edge segmentation when used in remote sensing images. This paper proposes a multi-attention-based semantic segmentation network for remote sensing images in order to address these problems. Specifically, we choose UNet as the baseline model, using a coordinate attention-based residual network in the encoder to improve the extraction capability of the backbone network for fine-grained features. We use a content-aware reorganization module in the decoder to replace the traditional upsampling operator to improve the network information extraction capability, and, in addition, we propose a fused attention module for feature map fusion after upsampling, aiming to solve the multi-scale problem. We evaluate our proposed model on the WHDLD dataset and our self-labeled Lu County dataset. The model achieved an mIOU of 63.27% and 72.83%, and an mPA of 74.86% and 84.72%, respectively. Through comparison and confusion matrix analysis, our model outperformed commonly used benchmark models on both datasets.
The microwave backscattering model is one of the most effective tools for surface soil moisture (SSM) inversion, which has strong theoretical support, but the inverse problem is difficult to solve. Advance in artificial intelligence offers possibilities to learn complex nonlinear relationships in a data-driven way, but it lacks physical mechanism. To combine the advantages of model-driven and data-driven methods, an SSM inversion approach that couples the AIEM-Oh model with deep neural networks (DNNs) was proposed in this study. DNNs with different inputs were trained with a large number of simulation data generated from the AIEM-Oh model, thus embedding physical mechanisms in the data-driven scheme. Two field experiments at different scales were carried out to evaluate the performances of the proposed approach over bare surfaces. The effects of polarization modes and prior knowledge of surface roughness on SSM inversion were explored, and the accuracy of the approach was compared with the existing methods. The results suggest that satisfactory accuracy was obtained by the proposed approach, the RMSE between the measured and estimated values of SSM was 0.03-0.04 cm 3 • cm −3 with prior knowledge of soil roughness, and the RMSE was 0.08-0.10 cm 3 • cm −3 without the prior soil roughness information. VV polarization was more sensitive to SSM over bare surfaces than VH polarization. Moreover, the approach showed stable performance in different experimental regions. The results demonstrate the capability and reliability of the coupled approach for SSM inversion over bare surfaces.
BackgroundGastric cancer (GC) is one of the most significant health problems worldwide. Some studies have reported associations between Phospholipase C epsilon 1 (PLCE1) single-nucleotide polymorphisms (SNPs) and GC susceptibility, but its relationship with GC prognosis lacked exploration, and the specific mechanisms were not elaborated fully yet. This study aimed to further explore the possible mechanism of the association between PLCE1 polymorphisms and GC.Materials and MethodsA case-control study, including 588 GC patients and 703 healthy controls among the Chinese Han population, was performed to investigate the association between SNPs of PLCE1 and GC risk by logistic regression in multiple genetic models. The prognostic value of PLCE1 in GC was evaluated by the Kaplan-Meier plotter. To explored the potential functions of PLCE1, various bioinformatics analyses were conducted. Furthermore, we also constructed the spatial structure of PLCE1 protein using the homology modeling method to analyze its mutations.ResultsRs3765524 C > T, rs2274223 A > G and rs3781264 T > C in PLCE1 were associated with the increased risk of GC. The overall survival and progression-free survival of patients with high expression of PLCE1 were significantly lower than those with low expression [HR (95% CI) = 1.38 (1.1–1.63), P < 0.01; HR (95% CI) = 1.4 (1.07–1.84), P = 0.01]. Bioinformatic analysis revealed that PLCE1 was associated with protein phosphorylation and played a crucial role in the calcium signal pathway. Two important functional domains, catalytic binding pocket and calcium ion binding pocket, were found by homology modeling of PLCE1 protein; rs3765524 polymorphism could change the efficiency of the former, and rs2274223 polymorphism affected the activity of the latter, which may together play a potentially significant role in the tumorigenesis and prognosis of GC.ConclusionPatients with high expression of PLCE1 had a poor prognosis in GC, and SNPs in PLCE1 were associated with GC risk, which might be related to the changes in spatial structure of the protein, especially the variation of the efficiency of PLCE1 in the calcium signal pathway.
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